📊 DPC Clustering Experiment Results
Reproduction of "Clustering by fast search and find of density peaks"
📄 Paper: "Clustering by fast search and find of density peaks"
👥 Authors: Alex Rodriguez and Alessandro Laio
🏛️ Journal: Science 344, 1492 (2014)
📈 Algorithm: Density Peaks Clustering (DPC)
🎯 Key Idea: Cluster centers are characterized by high density and large distance from points with higher density
👥 Authors: Alex Rodriguez and Alessandro Laio
🏛️ Journal: Science 344, 1492 (2014)
📈 Algorithm: Density Peaks Clustering (DPC)
🎯 Key Idea: Cluster centers are characterized by high density and large distance from points with higher density
Figure 2: Synthetic Data with 5 Density Peaks
Synthetic data with 5 density peaks of varying shapes and densities. Tests the algorithm's ability to detect non-spherical clusters with different densities.
Clustering Results
Shows the final clustering results. Different colors represent different clusters. Gray 'X' marks are halo/noise points. Yellow stars are cluster centers.
Decision Graph
Decision graph plotting δ (minimum distance to higher density point) vs ρ (local density). Cluster centers appear in the top-right corner (high δ and high ρ).
Gamma Values
Gamma values (γ = ρ × δ) sorted in descending order. Clear clusters show a sharp drop in γ values after the true centers.
Figure 3A: Two Crescent Moons
Two crescent-shaped clusters (moons dataset). Tests ability to detect non-convex clusters.
Clustering Results
Shows the final clustering results. Different colors represent different clusters. Gray 'X' marks are halo/noise points. Yellow stars are cluster centers.
Decision Graph
Decision graph plotting δ (minimum distance to higher density point) vs ρ (local density). Cluster centers appear in the top-right corner (high δ and high ρ).
Gamma Values
Gamma values (γ = ρ × δ) sorted in descending order. Clear clusters show a sharp drop in γ values after the true centers.
Figure 3B: 15 Overlapping Clusters
15 highly overlapping Gaussian clusters. Tests resolution in distinguishing many clusters.
Clustering Results
Shows the final clustering results. Different colors represent different clusters. Gray 'X' marks are halo/noise points. Yellow stars are cluster centers.
Decision Graph
Decision graph plotting δ (minimum distance to higher density point) vs ρ (local density). Cluster centers appear in the top-right corner (high δ and high ρ).
Gamma Values
Gamma values (γ = ρ × δ) sorted in descending order. Clear clusters show a sharp drop in γ values after the true centers.
Figure 3C: Three Concentric Circles
Three concentric circular clusters. Tests ability to detect nested structures.
Clustering Results
Shows the final clustering results. Different colors represent different clusters. Gray 'X' marks are halo/noise points. Yellow stars are cluster centers.
Decision Graph
Decision graph plotting δ (minimum distance to higher density point) vs ρ (local density). Cluster centers appear in the top-right corner (high δ and high ρ).
Gamma Values
Gamma values (γ = ρ × δ) sorted in descending order. Clear clusters show a sharp drop in γ values after the true centers.
Figure 3D: Three Curved Clusters
Three curved, non-linearly separable clusters. Tests performance on complex shapes.
Clustering Results
Shows the final clustering results. Different colors represent different clusters. Gray 'X' marks are halo/noise points. Yellow stars are cluster centers.
Decision Graph
Decision graph plotting δ (minimum distance to higher density point) vs ρ (local density). Cluster centers appear in the top-right corner (high δ and high ρ).
Gamma Values
Gamma values (γ = ρ × δ) sorted in descending order. Clear clusters show a sharp drop in γ values after the true centers.
Comparison: All Test Cases
Side-by-side comparison of DPC algorithm performance on all test cases from Figure 3.
Algorithm Performance Comparison
Summary plot showing DPC clustering results on all four test cases from Figure 3.